Page 26.732.5not know personally”. In addition, participants were asked to report the genders of the mentorand role model and indicated if that person works in the construction industry.Administration, Data Collection, and AnalysisThe survey was administered to a convenience sample of 828 students enrolled in undergraduate-level construction management courses at three universities (University 1, n = 286; University 2,n = 349; University 3, n = 193) during the spring semester of 2014. A total of 679 surveys werereturned, yielding a response rate of 82%. The intent of this study was to measure the self-efficacy and motivation of adult undergraduate construction management students. Participantswere classified as construction management students
learning program to college choice. The remaining two questions are open-ended and allow students to describe their favorite ENGR 102 HS design and build project andcomments about their teacher. Many of the Likert scale questions for the online survey wereobtained from the on-campus course evaluations handed out to undergraduates in the ENGR 102course and deal with the quality of instruction and content. Additional questions, those dealingwith self-efficacy, were selected from the Longitudinal Assessment of Engineering Self-Efficacy(LAESE) instrument measuring student self-efficacy [36]. The LAESE instrument is a validatedinstrument that was developed with NSF funding as part of the Assessing Women in Engineering(AWE) project and can be found at
] E. Fast and E. Horvitz, "Long-Term Trends in the Public Perception of Articial Intelligence," AAAI, vol. 31, no. 1, 2017.[2] M. Borrego, "Conceptual difficulties experienced by trained engineers learning educational research methods," Journal of Engineering Education, vol. 96, no. 2, pp. 91-102, 2007.[3] N. A. Mamaril, E. L. Usher, C. R. Li, D. R. Economy and M. S. Kennedy, "Measuring Undergraduate Students' Engineering Self-Efficacy: A Validation Study," Journal of Engineering Education, vol. 105, no. 2, pp. 366-395, 4 2016.[4] R. M. Marra and B. Bogue, "Women Engineering Students' Self Efficacy-A Longitudinal Multi- Institution Study," 2006.[5] J. S. Weedon, "Judging for Themselves: How Students Practice Engineering
discussions with participants. Interviews and focus groupswere digitally recorded and transcribed. A reflective analysis process was used to analyze andinterpret interviews and focus groups.Test of Students’ Science KnowledgeA student science content knowledge assessment aligned to the instructional goals of the researchcourse was developed and administered at the onset and conclusion of each part of the course.S-STEM SurveyThe S-STEM Student Survey measures student self-efficacy related to STEM content, interest inpursuing STEM careers, and the degree to which students implement 21st century learning skills.The survey was administered in a pre/post format at the beginning and end of each project year.FindingsResults are organized by evaluation
Paper ID #29565Effects of High School Dual Credit Introduction to Engineering Course onFirst-Year Engineering Student Self-Efficacy and the Freshman Experience(Evaluation)Ms. J. Jill Rogers, University of Arizona J. Jill Rogers is the assistant director for ENGR 102 HS at the University of Arizona. ENGR 102 HS is an AP-type, dual credit college level, introductory engineering course offered to high school students. In 2014, the ENGR 102 HS program won the ASEE best practices in K-12 and University partnerships award. Over the years Rogers has developed K-12 science summer camps, conducted K-12 educational re- search
. (2010). Measuring Engineering Design Self - Efficacy. Journal of Engineering Education, 99(1), 71-79.13. Kusurkar, R. A., Ten Cate, T. J., Vos, C. M. P., Westers, P., & Croiset, G. (2012). How motivation affects academic performance: A structural equation modelling analysis.14. Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-Motivation for Academic Attainment: The Role of Self-Efficacy Beliefs and Personal Goal Setting. American Educational Research Journal, 29(3), 663- 676.15. Schunk, D. H. (1990). Goal setting and self-efficacy during self-regulated learning. Educational Psychologist, 25, 71-86.16. R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical
organizations including the Society of Women Engineers(SWE), the Society of Hispanic Professional Engineers (SHPE), the Society of Asian Scientists andEngineers (SASE), the National Society of Black Engineers (NSBE) and ten times Outstanding ChapterAwardee, the American Chemical Society-Wright College Chapter. Doris promotes collaboration betweenK-12 schools, other community colleges, 4-year institutions, non-profit organizations, and industries.Doris’ current research is to design and implement practices that develop Community of Practice (CoP),Professional Identity, and Self-Efficacy to increase diversity in Engineering and Computer Science and tostreamline transfer from community colleges to 4-year institutions. ©American
is also known as visual-spatial skills and these are different from other forms ofintelligence such as verbal ability, reasoning ability, and memory skills. Spatial skills are linkedto professional and academic success [3], [4]. For example, when designing or constructing apumping station or piping systems within a treatment plant, it is always challenging to develop athree-dimensional mental picture of the space when looking at plan view and section views of aspace. Those who are skilled in developing that clear mental picture make fewer mistakes andare more efficient designers or constructors. Spatial training has been shown to have a strongimpact on developing these visual-spatial skills as measured by success on standardized
assesses or evaluates his/her own or others’ ideas or contribution to the topic discussed.Even though the number of units for positive indicator of this category was relatively high (148total) the critical ratio was relatively low (0.54) compared to other categories. This indicator wasoften identified when the students accepted or rejected others’ opinions with reasonableexplanations. For example: I see your point but I would say it can't be the case every time. Sometimes a project may not even need the advanced technologies to make it sustainable and it may pass the CHPS standards by using the simple green design measures only. (G3 W3)DiscussionResults of this study indicated small group format enabled students more equally
results show that students use a common set of problem-solving factors thatmotivate and guide the them through the solution process. This research can help engineeringeducators to more holistically understand the problem-solving process of engineering students.References[1] D. Bolden, P. Barmby, S. Raine, and M. Gardner, “How Young Children View Mathematical Representations: A Study Using Eye-Tracking Technology,” Educ. Res., vol. 57, no. 1, pp. 59–79, 2015.[2] A. Elby, “What students’ learning of representations tells us about constructivism,” J. Math. Behav., vol. 19, no. 4, pp. 481–502, 2000.[3] M. Hill and M. D. Sharma, “Students’ Representational Fluency at University: A Cross- Sectional Measure of How
participate in the program from the same cohorts. The study investigatesthe relationship between self-efficacy, pre-college academic preparedness measures and theeffect of these factors on early college success outcomes (e.g., term GPA) for URM students whoparticipated in STP as well as URM students who did not participate.LITERATURE REVIEWSelf-efficacy is defined as confidence in one’s ability to perform specific tasks or courses ofaction necessary to attain a specific goal or function in a specific capacity. (Bandura, 1997).When measuring self-efficacy respondents are asked to rate their level of confidence forattaining a specific goal. A student’s self-efficacy has an influence on the decisions that he/shemakes regarding their demonstrated efforts
needs. We not only investigated the low-stakesassessments, students’ perceived learning, but also their social learning metrics includingbelongingness and self-efficacy because belonging and self-efficacy in learning are knownimportant factors that positively influence students academic performance [7], and students’success should not be limited to only performance based measurements. Furthermore, we studieda new instrument (PAIM) based on the POUR model [8] that measured students’ perceivedaccessibility. PAIM was found to be predictive of SWD perceived learning [8].Background Perceivable Users can use their available senses, such as sight, hearing, and touch, to fully process information in their electronic
same design task was used in the post design challenge. During the pre andpost design tasks, students are encouraged to use the rubric to design with the limitations andopportunities of AM in mind.4. MetricsThe participants’ learning and use of DfAM after the intervention was evaluated using thefollowing metrics: 1) DfAM self-efficacy and 2) Score from the rubric evaluating theincorporation of DfAM concepts in design outcomes. Table 1. Validated tool used to measure change in DfAM self-efficacy [25] Opportunistic DfAM concepts Restrictive DfAM conceptsThe DfAM Self-efficacy table shown in Table 1. was validated by Prabhu et al. [25] and wasused to record changes in student DfAM self-efficacy. This information was collected in
InterviewsMSEN teachers, student participants, and mentors participated in either focus groups or interviewsto determine the program’s impact on the items outlined in the evaluation criteria. Semi-structuredinterview protocols were used to guide discussions with participants. Interviews and focus groupswere digitally recorded and transcribed. A reflective analysis process was used to analyze andinterpret interviews and focus groups.Test of Students’ Science KnowledgeA student science content knowledge assessment aligned to the instructional goals of the researchcourse was developed and administered at the onset and conclusion of each part of the course.S-STEM SurveyThe S-STEM Student Survey measures student self-efficacy related to STEM content
Paper ID #45360Impact of a Femalized Architecture, Engineering, and Construction KinestheticLearning Model on the AEC Career Knowledge, Self-efficacy, and OutcomeExpectations of African American Middle School GirlsMiss Mercy Folashade Fash, North Carolina A&T State University Mercy Fash is a dedicated and accomplished PhD candidate in the Applied Science and Technology program at North Carolina Agricultural and Technical State University (NC A&T). Her research is primarily focused on increasing racial and gender diversity in STEM careers, addressing critical gaps and promoting inclusivity in these fields. Mercy’s
-Lopez, Changes in Latino/a Adolescents’ Engineering Self-efficacy and Perceptions of Engineering After Addressing Authentic Engineering Design Challenges, in Proceedings of American Society for Engineering Education Annual Conference. 2015, ASEE: Seattle, WA. p. 1-14.18. Mejia, J.A., et al., Funds of Knowledge in Hispanic Students’ Communities and Households that Enhance Engineering Design Thinking, in Proceedings of American Society for Engineering Education Annual Conference. 2014, ASEE: Indianapolis, IN. p. 1-20.19. Olitsky, S., Structure, agency, and the development of students’ identities as learners. Cultural Studies of Science Education, 2006. 1(4): p. 745-766.20. Kennedy, M., The Ownership
measured the degree to which teachers’ lesson implementations showed evidence of theengineering design practices encouraged by the project, and students’ scores on the contentknowledge post-tests for each design task. The results are shown in Table 11 (for grade 5 tasks)and Table 12 (for grade 6 tasks).The results indicate that there were small to moderate positive correlations between teachers’implementation rubric scores and students’ knowledge post-test scores in both grades 5 and 6.These correlations ranged from a low of r = 0.14254 (for the relationship of teachers’ WaterFilter implementation scores and students’ Water Filter post-test scores) to a high of r = 0.45466(for the relationship of teachers’ Solar Tracker implementation scores and
writing self-efficacy assessment: Greater discrimination increases prediction. Measurement and Evaluation in Counseling and Development, 2001. 33: p. 214-221.32. Comrey, A.L. and J.W. Osborne, Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, & Evaluation, 2005. 10(7).33. Gorsuch, R.L., Factor analysis. 2nd ed. 1983, Hillsdale, NJ: Earlbaum.34. MacCallum, R.C., et al., Sample size in factor analysis. Psychological Methods, 1999. 4: p. 84-99.35. Rummel, R.J., Applied factor analysis. 1970, Evanston, IL: Northwestern University Press.36. Stevens, R., K. O'Connor, and L. Garrison. Engineering student identities in the navigation of the
a specific task such as problem solving or design.1 Results have indicated thatstudents with higher self-efficacy (a task-specific motivation2) have been shown to have improvedlearning and understanding in introductory engineering courses.3 Work focused on long-termgoals, such as graduating with an engineering degree, has shown that students who have higherexpectancies for their performance in engineering have significantly higher grade point averages(GPAs).4,5 Connections between these two scales of motivation have been proposed, yet little workhas been done to examine how these levels are connected and influence one another.6 Theoverarching purpose of our research is to understand the connection between multiple levels of
Learning Beliefs, Self-Efficacy for Learning& Performance, and learning strategies scales with Rehearsal, Elaboration, Organization,Metacognitive Self-Regulation, Time/Study Environmental Management, and Help Seeking. Inline with the desired measurement constructs for lifelong learning, the scales of Test Anxiety,Task Value, Peer Learning, and Effort Regulation were excluded. Also excluded was Critical Page 26.1176.7Thinking because alternate instruments in the larger research project measured this dimension.Analysis of findings from our MSLQ pilot found lower reliabilities than expected, and thatstudents demonstrated minimal engagement
somewhat amorphous concept such as entrepreneurial thinking and mindset. In this paper, the authors describe Kettering University’s efforts to measure faculty and student attitudes as we seek to infuse entrepreneurship across the curriculum. The paper discusses three specific measurement efforts. Our early efforts were formative and focused on student entrepreneurial mindset among engineering students studying entrepreneurship in a single course. Here we used measures of self-efficacy and locus of control as predictors of intention to start a business 2 3 4. Our second (and current) efforts focus on a pilot project designed to motivate faculty to alter their courses to include one or more of eleven
) course, became aware of the changes in their understandingof DET. Weekly reflection papers, weekly written pre and post tests and lesson plans were usedas data sources. A rubric linking the course outcomes with six major categories (engineering as adesign process, gender and diversity, societal relevance of engineering, technical self-efficacy,tinkering self-efficacy and transfer to classroom teaching) was developed to code text. Severalpasses through the data led to the refinements for the six categories that allowed the coding ofalmost all of the text. We specifically looked for shifts in understanding over a 15-week periodand an awareness that these shifts were taking place (e.g. “It’s not that I had a bad attitude abouttechnology to begin
entrepreneurshipin adults, our first psychosocial factor-based hypothesis is to examine the relative influence ofthis factor to the other five factors examined. Hypothesis 1: Late adolescent undergraduates who exhibit high self-efficacy will engage in more new- venturing activities than undergraduates who exhibit low self-efficacy.Need for AchievementThe need for achievement is the need to advance for measurable personal accomplishment.35Entrepreneurship researchers have examined the influence of need for achievement, also calledachievement orientation, on entrepreneurial success since the earliest entrepreneurship researchstudies.35 Schumpeter incorporated concepts of need for achievement into his early theories ofentrepreneurship and
], as well as self-efficacy and resilience. Therevised scale included modified items from Fisher and Peterson’s 2001 survey [20], additionalitems of our own construction, and several items based on work by van der Heijden [33],Charbonnier-Voiirin et al., [36], Bohle Carbonell et al., [35], and the General Self-Efficacy Scale(GSES-12) [37], [38].We were guided to include domain skills by the near-consensus in the adaptive expertiseliterature that adaptive expertise is built on top of subject-specific routine expertise. Ourproposed domain skill items address student perception of growth in their field, as well as theirability to pursue expertise and integrate new developments in the field [33], [35]. Innovativeskills by contrast focus on student
Designing andConducting Mixed Methods Research by J.W. Creswell & V.L. Plano Clark, 2007.ParticipantsThis study will focus on the experiences of first-year engineering students. These students areable to inform our research questions because they are the least removed from their precollegeengineering experiences and from the transition to college engineering programs. To the extentthat self-efficacy is important to persistence in engineering4, the mastery experiences of first-yearstudents will be more closely tied to their precollege experiences, whereas the masteryexperiences of upper-level engineering students will be derived from their college engineeringexperiences.Qualitative Data CollectionWe administered a survey on students’ demographic
orientation toward cultural differences 35 Learning self-efficacy instrument: confidence in self-directed learning25, 36, 37 Miville-Guzman Universality-Diversity Scale (MGUDS-S) survey – cultural competency38, 39 Need for Cognition Scale: self-directed learning measure40 Pittsburg Freshman Engineering Attitudes Survey (PFEAS) 41, 42 Situational Intrinsic Motivation Scale: base motivation measure 43 Student Self-Determination Scale (SDSS) 44 Student Thinking & Interacting Survey 27, 28Bland notes that quantitative data such as the IDI should be linked with qualitative information,because the IDI can show that movement is taking place along the
engineeringpersistence49,50. Performance/competence beliefs are broader than self-efficacy, which has beentraditionally measured as task-specific attainment51. Students’ beliefs about their ability toperform the practices of their discipline and understand the content of their discipline – whetherscience, math, or engineering – has an impact on their ability to see themselves as the kind ofperson who can legitimately participate in these areas52.Figure 1. Framework for students’ identification with engineering adapted from Hazari et al.16These three factors (recognition, interest, and performance/competence) comprise the identitymeasures developed in this work and are consistent with prior literature from psychology,sociology, science education, and engineering
. Thisforces students to (re-)enter the same harmful environments with the expectation of developingenough “grit” to “persist” [13]. These efforts place the responsibility on the most minoritized,with no focus on those from dominant identities who create/enable these environments. Creatingand sustaining more equitable and inclusive environments requires improving everyone’scultural competence (not just increasing sense of belonging and self-efficacy in those who aremost harmed).As more computing departments develop interventions to increase diversity, equity, andinclusion that target all students [2], [14], an instrument for measuring their impact beyondenrollment, retention, and graduation rates is needed. This work details the development andtesting
Paper ID #41956Defining Measurement Constructs for Assessing Learning in MakerspacesMr. Leonardo Pollettini Marcos, Purdue University Leonardo Pollettini Marcos is a 3rd-year PhD student at Purdue University’s engineering education program. He completed a bachelor’s and a master’s degree in Materials Engineering at the Federal University of Sao Carlos, Brazil. His research interests are in assessment instruments and engineering accreditation processes.Dr. Julie S. Linsey, Georgia Institute of Technology Dr. Julie S. Linsey is a Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute
). and sites). Entrepreneurial self–efficacy: Refining the measure. Entrepreneurship Upper-division entrepreneurship course theory and Practice, 33(4), 965-988. SEARCHING: Identification of an idea or opportunity. 2. Pan, X. (2020). Technology acceptance, technological self-efficacy, and Pre-Collection Post-Collection